Introducing risk inequality metrics in tuberculosis policy development

M. Gabriela M. Gomes, Juliane F. Oliveira, Adelmo Bertolde, Diepreye Ayabina, Tuan Anh Nguyen, Ethel L. Maciel, Raquel Duarte, Binh Hoa Nguyen, Priya B. Shete, Christian Lienhardt

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
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Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as the single process where heterogeneity most fundamentally impacts model outputs, due to selection imposed by dynamic forces of infection. We introduce concrete metrics of risk inequality, demonstrate their utility in mathematical models, and pack the information into a risk inequality coefficient (RIC) which can be calculated and reported by national tuberculosis programs for use in policy development and modeling.

Original languageEnglish
Article number2480
Number of pages12
JournalNature Communications
Publication statusPublished - 6 Jun 2019


  • policy development
  • tuberculosis
  • World Health Organization (WHO)
  • predictive models


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